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. 2025 Jan 10:15:04011.
doi: 10.7189/jogh.15.04011.

Country-specific key lifestyle factors and health outcomes for resource allocation in the general population: a network analysis across 29 countries

Jiaying Li  1   2 Daniel Yee Tak Fong  1 Kris Yuet Wan Lok  1 Janet Yuen Ha Wong  3 Mandy Man Ho  1 Edmond Pui Hang Choi  1 Vinciya Pandian  2 Patricia M Davidson  4 Wenjie Duan  5 Marie Tarrant  6 Jung Jae Lee  1 Chia-Chin Lin  1 Oluwadamilare Akingbade  7   8 Khalid M Alabdulwahhab  9 Mohammad Shakil Ahmad  10 Mohamed Alboraie  11 Meshari A Alzahrani  12 Anil S Bilimale  13 Sawitree Boonpatcharanon  14 Samuel Byiringiro  3 Muhammad Kamil Che Hasan  15 Luisa Clausi Schettini  16 Walter Corzo  17 Josephine M De Leon  18 Anjanette S De Leon  18 Hiba Deek  19 Fabio Efficace  20 Mayssah A El Nayal  21 Fathiya El-Raey  22 Eduardo Ensaldo-Carrasco  23 Pilar Escotorin  24 Oluwadamilola Agnes Fadodun  25 Israel Opeyemi Fawole  26 Yong-Shian Shawn Goh  27 Devi Irawan  28 Naimah Ebrahim Khan  29 Binu Koirala  2 Ashish Krishna  30 Cannas Kwok  31 Tung Thanh Le  32 Daniela Giambruno Leal  33 Miguel Ángel Lezana-Fernández  34 Emery Manirambona  35 Leandro Cruz Mantoani  36 Fernando Meneses-González  34 Iman Elmahdi Mohamed  37 Madeleine Mukeshimana  38 Chinh Thi Minh Nguyen  32 Huong Thi Thanh Nguyen  32 Khanh Thi Nguyen  32 Son Truong Nguyen  32 Mohd Said Nurumal  15 Aimable Nzabonimana  39 Nagla Abdelrahim Mohamed Ahmed Omer  40 Oluwabunmi Ogungbe  2 Angela Chiu Yin Poon  41 Areli Reséndiz-Rodriguez  42 Busayasachee Puang-Ngern  14 Ceryl G Sagun  18 Riyaz Ahmed Shaik  10 Nikhil Gauri Shankar  43 Kathrin Sommer  20 Edgardo Toro  33 Hanh Thi Hong Tran  32 Elvira L Urgel  18 Emmanuel Uwiringiyimana  35 Tita Vanichbuncha  14 Naglaa Youssef  44
Affiliations

Country-specific key lifestyle factors and health outcomes for resource allocation in the general population: a network analysis across 29 countries

Jiaying Li et al. J Glob Health. .

Abstract

Background: We aimed to identify the central lifestyle, the most impactful among lifestyle factor clusters; the central health outcome, the most impactful among health outcome clusters; and the bridge lifestyle, the most strongly connected to health outcome clusters, across 29 countries to optimise resource allocation for local holistic health improvements.

Methods: From July 2020 to August 2021, we surveyed 16 461 adults across 29 countries who self-reported changes in 18 lifestyle factors and 13 health outcomes due to the pandemic. Three networks were generated by network analysis for each country: lifestyle, health outcome, and bridge networks. We identified the variables with the highest bridge expected influence as central or bridge variables. Network validation included nonparametric and case-dropping subset bootstrapping, and centrality difference tests confirmed that the central or bridge variables had significantly higher expected influence than other variables within the same network.

Results: Among 87 networks, 75 were validated with correlation-stability coefficients above 0.25. Nine central lifestyle types were identified in 28 countries: cooking at home (in 11 countries), food types in daily meals (in one country), less smoking tobacco (in two countries), less alcohol consumption (in two countries), less duration of sitting (in three countries), less consumption of snacks (in five countries), less sugary drinks (in five countries), having a meal at home (in two countries), taking alternative medicine or natural health products (in one country). Six central health outcomes were noted among 28 countries: social support received (in three countries), physical health (in one country), sleep quality (in four countries), quality of life (in seven countries), less mental burden (in three countries), less emotional distress (in 13 countries). Three bridge lifestyles were identified in 19 countries: food types in daily meals (in one country), cooking at home (in one country), overall amount of exercise (in 17 countries). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P < 0.05).

Conclusions: In 29 countries, cooking at home, less emotional distress, and overall amount of exercise emerged as common central lifestyle, health outcome, and bridge lifestyle factors, respectively. However, notable regional variations necessitate tailored interventions and resource allocations to effectively address unique local key variables and promote holistic health in each locale. The study's cross-sectional design and self-reported data may limit generalisability, emphasising the need for cautious interpretation and further longitudinal research.

Keywords: global; across-country comparisons; lifestyle; health outcomes; network analysis.

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Conflict of interest statement

Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.

Figures

Figure 1
Figure 1
Network structure and centrality difference test of lifestyles (Panels A–B), health outcomes (Panels C–D), and combined (Panels E–F) in mainland China. The abbreviations of nodes in Panels A, C, and E can be found in Table 1. In Panels B, D, and F, a grey cell indicates that there is no significant difference between the corresponding two variables. A dark cell indicates that there is a significant difference between the corresponding two variables at 5% level of significance. A white cell displays the value of the expected influence or bridge expected influence.
Figure 2
Figure 2
Central lifestyles, central health outcomes, and bridge lifestyles by country.

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